
"""simple docstring"""


import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__ = None	,lowerCAmelCase__ = None	,lowerCAmelCase__ = None	,):
			if config_name_or_path is None:
						lowerCamelCase_					=  '''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

			if generator_tokenizer_name_or_path is None:
						lowerCamelCase_					=  generator_name_or_path

			if question_encoder_tokenizer_name_or_path is None:
						lowerCamelCase_					=  question_encoder_name_or_path

			lowerCamelCase_					=  RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

			# Save model.
			lowerCamelCase_					=  RagConfig.from_pretrained(lowerCAmelCase__						)
			lowerCamelCase_					=  AutoConfig.from_pretrained(lowerCAmelCase__						)
			lowerCamelCase_					=  AutoConfig.from_pretrained(lowerCAmelCase__						)

			lowerCamelCase_					=  gen_config
			lowerCamelCase_					=  question_encoder_config

			lowerCamelCase_					=  model_class.from_pretrained_question_encoder_generator(
			    lowerCAmelCase__	,lowerCAmelCase__	,config=lowerCAmelCase__						)
			rag_model.save_pretrained(lowerCAmelCase__						)

			# Sanity check.
			model_class.from_pretrained(lowerCAmelCase__						)

			# Save tokenizers.
			lowerCamelCase_					=  AutoTokenizer.from_pretrained(lowerCAmelCase__						)
			gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''						)
			lowerCamelCase_					=  AutoTokenizer.from_pretrained(lowerCAmelCase__						)
			question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''						)


if __name__ == "__main__":
						A_    							=					argparse.ArgumentParser()
						parser.add_argument(
						    """--model_type""",
						    choices=["""rag_sequence""", """rag_token"""],
						    required=True,
						    type=str,
						    help="""RAG model type: rag_sequence, rag_token""",
						)
						parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
						parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
						parser.add_argument(
						    """--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
						)

						parser.add_argument(
						    """--generator_tokenizer_name_or_path""",
						    type=str,
						    help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
						)
						parser.add_argument(
						    """--question_encoder_tokenizer_name_or_path""",
						    type=str,
						    help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
						)
						parser.add_argument(
						    """--config_name_or_path""",
						    type=str,
						    help=(
						        """Identifier of the model config to use, if not provided, resolves to a base config for a given"""
						        """ ``model_type``"""
						    ),
						)

						A_    							=					parser.parse_args()

						A_    							=					Path(args.dest)
						dest_dir.mkdir(exist_ok=True)

						consolidate(
						    args.model_type,
						    args.generator_name_or_path,
						    args.question_encoder_name_or_path,
						    dest_dir,
						    args.config_name_or_path,
						    args.generator_tokenizer_name_or_path,
						    args.question_encoder_tokenizer_name_or_path,
						)











"""simple docstring"""
def 							A						(				__snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    limit + 1
       __magic_name__      				=    [0] * limit
       for first_term in range(1 ,     __snake_case  ):
              for n in range(__snake_case ,     __snake_case ,     __snake_case  ):
                     __magic_name__      				=    first_term + n / first_term
                     if common_difference % 4:  # d must be divisble by 4
                            continue
                     else:
                            common_difference /= 4
                            if (
                                first_term > common_difference
                                and first_term < 4 * common_difference
                            ):  # since x,y,z are positive integers
                                   frequency[n] += 1  # so z>0 and a>d ,also 4d<a

       __magic_name__      				=    sum(1 for x in frequency[1:limit] if x == 1_0  )

       return count


if __name__ == "__main__":
 print(f"""{solution() = }""")